Process control systems, like those used in chemical, petroleum or other processes, typically include one or more process controllers and input/output (I/O) devices communicatively coupled to at least one host or operator workstation and to one or more field devices via analog, digital or combined analog/digital buses. The field devices, which may be, for example, valves, valve positioners, switches and transmitters (e.g., temperature, pressure and flow rate sensors), perform process control functions within the process such as opening or closing valves and measuring process control parameters. The process controllers receive signals indicative of process measurements made by the field devices, process this information to implement a control routine, and generate control signals that are sent over the buses or other communication lines to the field devices to control the operation of the process. In this manner, the process controllers may execute and coordinate control strategies using the field devices via the buses and/or other communication links.
Process information from the field devices and the controllers may be made available to one or more applications (i.e., software routines, programs, etc.) executed by the operator workstation (e.g., a processor-based system) to enable an operator to perform desired functions with respect to the process, such as viewing the current state of the process (e.g., via a graphical user interface), evaluating the process, modifying the operation of the process (e.g., via a visual object diagram), etc. Many process control systems also include one or more application stations (e.g., workstations) which are typically implemented using a personal computer, laptop, or the like and which are communicatively coupled to the controllers, operator workstations, and other systems within the process control system via a local area network (LAN). Each application station may include a graphical user interface that displays the process control information including values of process variables, values of quality parameters associated with the process, process fault detection information, and/or process status information.
Typically, displaying process information in the graphical user interface is limited to the display of a value of each process variable associated with the process. Additionally, some process control systems may characterize simple relationships between some process variables to determine quality metrics associated with the process. However, in cases where a resultant product of the process does not conform to predefined quality control metrics, the process and/other process variables can only be analyzed after the completion of a batch, a process, and/or an assembly of the resulting product. While viewing the process and/or quality variables upon the completion of the process enables improvements to be implemented to the manufacturing or the processing of subsequent products, these improvements are not able to remediate the current completed products, which are out-of-spec.
This problem is particularly acute in batch processes, that is, in batch process control systems that implement batch processes. As is known, batch processes typically operate to process a common set of raw materials together as a “batch” through various numbers of stages or steps, to produce a product. Multiple stages or steps of a batch process may be performed in the same equipment, such as in a tank, while others of the stages or steps may be performed in other equipment. Because the same raw materials are being processed differently over time in the different stages or steps of the batch process, in many cases within a common piece of equipment, it is difficult to accurately determine, during any stage or step of the batch process, whether the material within the batch is being processed in a manner that will likely result in the production of the end product that has desired or sufficient quality metrics. That is, because the temperature, pressure, consistency, pH, or other parameters of the materials being processed changes over time during the operation of the batch, many times while the material remains in the same location, it is difficult to determine whether the batch processes is operating at any particular time during the batch run in a manner that is this likely to produce an end product with the desired quality metrics.
One known method of determining whether a currently operating batch is progressing normally or within desired specifications (and is thus likely to result in a final product having desired quality metrics) compares various process variable measurements made during the operation of the on-going batch with similar measurements taken during the operation of a “golden batch.” In this case, a golden batch is a predetermined, previously run batch selected as a batch run that represents the normal or expected operation of the batch and that results in an end product with desired quality metrics. However, batch runs of a process typically vary in temporal length, i.e., vary in the time that it takes to complete the batch, making it difficult to know which time, within the golden batch, is most applicable to the currently measured parameters of the on-going batch. Moreover, in many cases, batch process variables can vary widely during the batch operation, as compared to those of a selected golden batch, without a significant degradation in quality of the final product. As a result, it is often difficult, if not practically impossible, to identify a particular batch run that is capable of being used in all cases as the golden batch to which all other batch runs should be compared.
A method of analyzing the results of on-going batch processes that overcomes one of the problems of using a golden batch involves creating a statistical model for the batch. This technique involves collecting data for each of a set of process variables (batch parameters) from a number of different batch runs of a batch process and identifying or measuring quality metrics for each of those batch runs. Thereafter, the collected batch parameters and quality data is used to create a statistical model of the batch, with the statistical model representing the “normal” operation of the batch that results in desired quality metrics. This statistical model of the batch can then be used to analyze how different process variable measurements made during a particular batch run statistically relate to the same measurements within the batch runs used to develop the model. For example, this statistical model may be used to provide an average or a median value of each measured process variable, and a standard deviation associated with each measured process variable at any particular time during the batch run to which the currently measured process variables can be compared. Moreover, this statistical model, may be used to predict how the current state of the batch will effect or relate to the ultimate quality of the batch product produced at the end of the batch.
Generally speaking, this type of batch modeling requires huge amounts of data to be collected from various sources such as transmitters, control loops, analyzers, virtual sensors, calculation blocks and manual entries. Most of the data is stored in continuous data historians. However, significant amounts of data and, in particular, manual entries, are usually associated with process management systems. Data extraction from both of these types of systems must be merged to satisfy model building requirements. Moreover, as noted above, a batch process normally undergoes several significantly different stages, steps or phases, from a technology and modeling standpoint. Therefore, a batch process is typically sub-divided with respect to the phases, and a model may be constructed for each phase. In this case, data for the same phase or stage, from many batch runs, is grouped to develop the statistical model for that phase or stage. The purpose of such a data arrangement is to remove or alleviate process non-linearities. Another reason to develop separate batch models on a stage, phase or other basis is that, at various different stages of a batch, different process parameters are active and are used for modeling. As a result, a stage model can be constructed with a specific set of parameters relevant for each particular stage to accommodate or take into account only the process parameters relevant at each batch stage. For example at a certain stage, additives may be added to the main batch load, and process parameters pertaining to those additives do not need to be considered in any preceding batch stage, but are relevant to the batch stage at which the additives are added.
However in creating this statistical batch model, it is still necessary to deal with the fact that different batch runs typically span different lengths of time. This phenomena is based on a number of factors such as, for example, different wait times associated with operators taking manual actions within the batch runs, different ambient conditions that require longer or shorter heating or other processing times, variations in raw material compositions that lead to longer or shorter processing times during a batch run, etc. In fact, it is normal that the data trend for a particular process variable spans a different length of time in different batch runs, and therefore that common batch landmarks in the different batch process runs have time shifted locations with respect to one another. To create a valid statistical model, however, the data for each stage, operation, or phase of a batch must be aligned with comparable data from the same stage, operation or phase of the other batches used to create the model. Thus, prior to using data measured during runs of a batch process to create a statistical model for use in modeling and analyzing the batch process, it is necessary to align the batch data from the different batch runs to a common time frame. Techniques for performing such alignment of batch data are disclosed in U.S. patent application Ser. No. 12/784,689, entitled “On-Line Alignment Of A Process Analytical Model With Actual Batch Operation,” filed May 21, 2010, the disclosure of which is hereby incorporated by reference as if fully set forth herein. Once aligned, the batch data may be used in conjunction with analytic tools such as principal component analysis (PCA) and projection to latent structures (PLS) to develop models of the batch process that may be used to model and analyze further runs of the batch process.
The on-line use of analytic tools such as PCA and PLS techniques for fault detection and prediction of quality parameters has, in many instances, been limited to continuous processes in which a single product is produced. In such instances, the process is often treated as a single unit with a fixed set of measurements and lab analysis. For these types of processes, a single PCA or PLS model may be developed and applied in an on-line environment. However, to address the requirements of continuous or batch processes in which multiple products are produced using one or more pieces of plant equipment, each having its own set of instrumentation and quality parameters, a more general approach must be taken in developing a model off-line and in thereafter applying on-line analytics.
Applying on-line analytic tools to continuous and batch processes involves several challenges. First, in a batch operating environment, a product may be produced using numerous pieces of equipment that may be run in series, in parallel, or in a hybrid configuration having some equipment run in series and some in parallel. The equipment used in manufacturing and the associated process operating conditions depend on the product that is manufactured. Different lab and field measurements may be used at various points in the manufacturing process for one way of manufacturing a product versus another, or for manufacturing different products, which complicates model development. Similarly, a continuous operating environment also may involve multiple major pieces of equipment arranged in different configurations. The processing associated with each piece of equipment, and associated process measurements and control, in some cases, may vary as processing conditions change with throughput or with the product that is being processed.
Thus, tools designed to support on-line analytics for process modeling must take account of the product being produced, the equipment arrangements that may be used to make the product, and the different operating conditions and associated field and lab measurement needed to manufacture the product. Prior modeling approaches employed a single aggregate model for a process which did not allow for changing operating conditions and associated field and lab measurements needed in connection with modeling processes employing multiple pieces of equipment or producing multiple different products.